CN115470399A - ID (identity) communication method, device, equipment and storage medium based on big data - Google Patents
ID (identity) communication method, device, equipment and storage medium based on big data Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
Abstract
The invention provides an ID (identity) communication method, device, equipment and storage medium based on big data. The ID communication method based on the big data comprises the following steps: step S1: acquiring behavior data of different IDs, wherein the behavior data comprises various incidence relation information, and the credibility of each edge between every two IDs is respectively calculated by taking the same incidence relation between different IDs as one edge; step S2: selecting a reliability threshold value, and filtering edges with reliability lower than the threshold value; and step S3: calculating the association degree score between every two IDs; and step S4: sorting the candidate associated IDs of each ID according to the magnitude of the association degree score; step S5: and selecting the candidate association ID corresponding to the maximum association degree score as the getting-through ID of the ID. The method and the device are based on big data and graph computing technology, and the IDs of different systems are correlated, so that the IDs belong to the same device or a natural person user.
Description
Technical Field
The invention relates to the technical field of graph calculation, in particular to an ID (identity) getting-through method based on big data.
Background
The vast amount of data users generate in the process of participating in these online services. Because different systems adopt different identifications for users or equipment, the users or the equipment cannot be interconnected and communicated with each other. If the data of different systems can be communicated on the premise of ensuring the data safety, the user and the equipment can be analyzed more accurately and comprehensively, so that the future behavior of the user can be predicted more accurately, accurate content push and recommendation are carried out on the user, and huge commercial value is generated.
In the prior art, an intermediate ID needs to be generated between two systems, and a user carries the ID when switching between the two systems, so that data among different systems is communicated. Specifically, the data communication principle in the prior art is as follows: 1. when a user accesses app (or site) a, a gives a user ID uida and gives the access behavior ID sid, and records a data log loga including the following ID-pair: (uida, sid); 2. when a user jumps from app (or site) A to B, B gives a user ID uidb, and the sid is carried to B, and records a data log logb containing the following ID-pair: (uidb, sid); the ID punch-through system takes the data loga and logb, and associates uida and uidb by the same sid.
The data communication method in the prior art has the following defects: ID call-through requires relying on intermediate id;2. when jumping between systems, the intermediate id needs to be carried, and if the intermediate id is lost or an obstacle is encountered, the communication cannot be carried out.
Disclosure of Invention
In order to overcome the technical defects, a first aspect of the present invention provides an ID punching method based on big data, including:
step S1: acquiring behavior data of different IDs, wherein the behavior data comprises various incidence relation information, the same incidence relation between the different IDs is taken as one edge, the credibility of each edge between every two IDs is respectively calculated, and the credibility is in inverse proportion to the product of the number of different types of IDs associated with the edge;
for example, assuming that the kth edge is associated with two types of IDs, namely ida and idb, the number of IDs contained under ida is Count (ida), and the number of IDs contained under idb is Count (idb), the reliability P of the edge is k Comprises the following steps:
wherein, P k Representing the reliability of the kth edge, wherein Count (ida) and Count (idb) represent the number of a-type IDs and b-type IDs associated with the edge;
step S2: selecting a reliability threshold value, and filtering edges with reliability lower than the threshold value;
and step S3: calculating the association degree score between every two IDs:
wherein i and j represent two different IDs, P k (i, j) represents the confidence of each edge between i and j, K represents the number of edges between i and j, and Score (i, j) represents the relevance Score between i and j, wherein the relevance Score is proportional to the number of edges between IDs and the confidence of each edge;
and step S4: sorting the candidate associated IDs of each ID according to the degree of association score;
step S5: selecting a candidate association ID corresponding to the maximum association degree score as a call-through ID of the ID:
AssocID(i)＝argmax j Score(i，j)，
wherein i and j represent two different IDs respectively, and j is a candidate associated ID corresponding to the maximum association degree score, namely j is the call-through ID of i. This step is ID-mapping: and associating the IDs of different systems for identifying the IDs belonging to the same equipment or a natural human user. If a plurality of maximum values exist at the same time, all the maximum values are determined as the opening ID of the ID.
Further, the association relationship includes, but is not limited to, device information, spatio-temporal information, and the like.
Further, the device information includes, but is not limited to, hardware information including, but not limited to, brand, model, screen size, sensor model, etc., and software information including, but not limited to, browser ua, operating system version, etc., and temporal-spatial information including, but not limited to, time, geographical location information, ip address, etc.
A second aspect of the present invention provides an ID punch-through device based on big data, including:
the credibility calculation unit is used for acquiring behavior data of different IDs (identities), wherein the behavior data comprises various association relation information, the same association relation between different IDs is used as one edge, the credibility of each edge between every two IDs is respectively calculated, and the credibility is in inverse proportion to the product of the number of different types of IDs associated with the edge;
for example, assuming that the kth edge is associated with two types of IDs, namely ida and idb, the number of IDs contained under ida is Count (ida), and the number of IDs contained under idb is Count (idb), the reliability P of the edge is k Comprises the following steps:
wherein, P k Representing the reliability of the kth edge, wherein Count (ida) and Count (idb) represent the number of a-type IDs and b-type IDs associated with the edge; the filtering unit is used for selecting a reliability threshold value and filtering edges with reliability lower than the threshold value;
the association degree calculating unit is used for calculating the association degree score between every two IDs:
wherein i and j represent two different IDs, P k (i, j) represents the confidence of each edge between i and j, K represents the number of edges between i and j, and Score (i, j) represents the relevance Score between i and j;
the sorting unit is used for sorting the candidate associated IDs of each ID according to the magnitude of the association degree score;
and the association unit is used for selecting the candidate association ID corresponding to the maximum association degree score as the getting-through ID of the ID:
AssocID(i)＝argmax j Score(i，j)，
wherein i and j represent two different IDs respectively, and j is a candidate association ID corresponding to the maximum value of the association degree score, namely j is the getting-through ID of i.
Preferably, the association relationship includes, but is not limited to, device information, spatio-temporal information, and the like.
Preferably, the device information includes, but is not limited to, hardware information including, but not limited to, brand, model, screen size, sensor model, and the like, and software information including, but not limited to, browser ua, operating system version, and the like, and the spatio-temporal information including, but not limited to, time, geographical location information, ip address, and the like.
A third aspect of the present invention provides an electronic apparatus comprising: the ID communication method based on the big data comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the computer program is executed by the processor, the ID communication method based on the big data is realized.
A fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the above-mentioned big-data-based ID punch-through method.
The terms in this application are explained as follows:
(1) ID: identification, identification. When a user or a device accesses the system, the system records the access behavior in a log form, and distinguishes different users and devices by using the ID. Types of IDs include, but are not limited to, the following, for example: among apps: the method comprises the following steps of using, accounting, web-side cookie and session, IMEI, idfa, mac and android of mobile phone equipment, openid and unionid of WeChat, paliuray _ id, and display impression-id and click-id of advertisements; and so on.
(2) Side: associative links between different IDs include, but are not limited to: device information (hardware information: brand, model, screen size, sensor model \8230;, software information: browser ua, operating system version), spatiotemporal information (time, geographical location information, ip address, \8230;). The present application regards the same association relationship between different IDs as one edge, for example, assuming that "brand" covers X, Y, and Z, if two IDs both possess the same association relationship of X, X can be regarded as one edge. For example, if the number of IDs under the class a ID (ida) associated with X is 2, the number of IDs under the class b ID (idb) associated with X is 5, and 2 × 5=10 associations of ida and idb can be formed by X, the reliability of the edge (X) is =1/10=0.1.
(3) Candidate association ID: the candidate associate ID indicates that the association between IDs is based on reliability, and there may be a plurality of candidate associate IDs, and as time goes by, the association reliability between IDs changes, and the association also changes.
After the technical scheme is adopted, compared with the prior art, the method has the following beneficial effects:
each behavior data of the user carries a large amount of information (equipment information, space-time information and the like), and the ID of different systems can be communicated as a side, and the IDs of different systems are associated, so that the IDs belong to the same equipment or natural users. The method and the device are based on big data and graph computing technology, existing data can be fully mined and utilized, intermediate IDs do not need to be generated additionally, and the data is more valuable while cost is saved.
Drawings
FIG. 1 is a schematic diagram of graph computation in an embodiment of the present application;
fig. 2 is a flowchart of an ID punch-through method based on big data in an embodiment of the present application.
Detailed Description
The advantages of the invention are further illustrated in the following description of specific embodiments in conjunction with the accompanying drawings. It is to be understood by persons skilled in the art that the following detailed description is illustrative and not restrictive, and is not to be taken as limiting the scope of the invention.
For example, as shown in fig. 1, in the present embodiment, a device ID (the device ID includes a1, a2, and a 3) is used as ida, a cookie ID (the cookie ID includes b1, b2, and b 3) is used as idb, and an ID punch-through between the device ID and the cookie ID is taken as an example below, and ip + ua is selected as an edge (the edge includes e1, e2, and e 3) to perform ID punch-through, as shown in fig. 2, the ID punch-through method includes steps 1 to 5:
step 1: acquiring behavior data of different IDs, wherein the behavior data comprises various association relation information, the same association relation between different IDs is taken as an edge, the credibility of each edge between every two IDs is respectively calculated, and the credibility is in inverse proportion to the product of the number of different types of IDs associated with the edge:
wherein, P k Representing the confidence of the kth edge, count (ida) and Count (idb) represent the number of class-a IDs and class-b IDs with which the edge is associated.
The edge e1 associates 2 device IDs (a 1 and a 2) (then Count (ida) = 2) and 1 cookie ID (b 1) (then Count (idb) = 1), and thus the reliability P of e1 1 =0.5, for the same reason: e2 confidence level P 2 Confidence P of =0.5, e3 3 ＝1.0。
Step 2: and selecting a reliability threshold value, and filtering edges with reliability lower than the threshold value.
Preferably, a confidence threshold of 0.1,0.1 ensures that the number of certain types of IDs associated by the edge does not exceed 10, so that many associations that are meaningless for ID communication can be reduced. After filtering, the edges e1, e2, and e3 remain in this embodiment.
And step 3: calculating the association degree score between every two IDs:
wherein i and j represent two different IDs, P k (i, j) represents the confidence of each edge between i and j, K represents the number of edges between i and j, and Score (i, j) represents the relevance Score between i and j. The affinity score is proportional to the number of edges between IDs, the confidence of each edge.
The edges between a1 and b1 are e1 and e2, again because of the confidence P of e1 1 Reliability P of =0.5, e2 2 =0.5, so the association score of a1 and b1 score (a 1, b 1) = P 1 +P 2 ＝1.0。
The edge between a1 and b2 is e2, again because of the confidence P of e2 2 =0.5, so the association score of a1 and b2 score (a)1,b2)＝0.5。
Similarly, the association score between a2 and b1, score (a 2, b 1) = P 1 ＝0.5。
Similarly, the association score between a3 and b3 score (a 3, b 3) = P 3 ＝1.0。
And 4, step 4: the candidate associate IDs for each ID are ranked according to the magnitude of the association score.
The candidate associate IDs of a1 are b1 (association score of 1.0) and b2 (association score of 0.5).
The candidate affiliation ID of a2 is b1 (the affiliation score is 0.5).
The candidate associate ID of a3 is b3 (association score is 1.0).
And 5: selecting a candidate association ID corresponding to the maximum association degree score as a call-through ID of the ID:
AssocID(i)＝argmax j Score(i,j)，
wherein i and j represent two different IDs respectively, and j is a candidate associated ID corresponding to the maximum association degree score, namely j is the call-through ID of i. This step associates the IDs of different systems to identify whether the IDs belong to the same device or a natural person user.
The order is sorted to obtain b1 as the opening ID (namely the association ID) of a 1.
The same is true for: the opening ID (namely the association ID) of a2 is b1; the opening ID (i.e., association ID) of a3 is b3.
The flowchart in fig. 2 illustrates the architecture, functionality, and operations that may be implemented in accordance with the disclosed methods. In this regard, each block in the flowchart may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s).
Another embodiment of the present application further provides an ID punch-through device based on big data, which includes:
the credibility calculation unit is used for acquiring behavior data of different IDs (identities), wherein the behavior data comprises various association relation information, the same association relation between different IDs is used as one edge, the credibility of each edge between every two IDs is respectively calculated, and the credibility is in inverse proportion to the product of the number of different types of IDs associated with the edge;
for example, assuming that the kth edge is associated with two types of IDs, namely ida and idb, the number of IDs contained under ida is Count (ida), and the number of IDs contained under idb is Count (idb), the reliability P of the edge is k Comprises the following steps:
wherein, P k Representing the credibility of the kth edge, wherein Count (ida) and Count (idb) represent the number of the class a IDs and the class b IDs associated with the edge; the filtering unit is used for selecting a reliability threshold value and filtering edges with reliability lower than the threshold value;
a relevancy calculating unit for calculating relevancy scores between each two IDs:
wherein i and j represent two different IDs, P k (i, j) represents the confidence of each edge between i and j, K represents the number of edges between i and j, and Score (i, j) represents the relevance Score between i and j;
the sorting unit is used for sorting the candidate associated IDs of each ID according to the magnitude of the association degree score;
and the association unit is used for selecting the candidate association ID corresponding to the maximum association degree score as the getting-through ID of the ID:
AssocID(i)＝argmax j Score(i，j)，
wherein i and j represent two different IDs respectively, and j is a candidate association ID corresponding to the maximum value of the association degree score, namely j is a get-through ID of i.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the ID opening device based on big data in this embodiment is only illustrated by the above-mentioned division of each functional unit, and in practical applications, the above-mentioned function allocation may be performed by different functional units according to needs, that is, the internal structure of the device is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. For the specific working processes of the units and modules in the system, reference may be made to the corresponding processes in the foregoing method embodiments, which are not described herein again.
Another embodiment of the present application also provides an electronic device, including: the ID communication method based on the big data comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the computer program is executed by the processor, the ID communication method based on the big data is realized.
The electronic device includes, but is not limited to, a user device, a network device, or a device formed by integrating a user device and a network device through a network. The user equipment includes, but is not limited to, any mobile electronic product, such as a smart phone, a tablet computer, etc., which can perform human-computer interaction with a user through a touch panel, and the mobile electronic product may employ any operating system, such as an android operating system, an IOS operating system, etc. The network device includes an electronic device capable of automatically performing numerical calculation and information processing according to a preset or stored instruction, and the hardware includes but is not limited to a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like. The network device comprises but is not limited to a computer, a network host, a single network server, a plurality of network server sets or a cloud formed by a plurality of servers; here, the Cloud is composed of a large number of computers or web servers based on Cloud Computing (Cloud Computing), which is a kind of distributed Computing, one virtual supercomputer consisting of a collection of loosely coupled computers.
Another embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements the above big data-based ID punch-through method.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as a punch card or an in-groove protruding structure with instructions stored thereon, and any suitable combination of the foregoing. The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
It should be noted that the embodiments of the present invention have been described in a preferred embodiment and not limited to the embodiments, and those skilled in the art may modify and modify the above-disclosed embodiments to equivalent embodiments without departing from the scope of the present invention.
Claims (6)
1. An ID communication method based on big data is characterized by comprising the following steps:
step S1: acquiring behavior data of different IDs, wherein the behavior data comprises various incidence relation information, the same incidence relation between different IDs is taken as one edge, the credibility of each edge between every two IDs is respectively calculated, and the credibility is in inverse proportion to the product of the number of different types of IDs associated with the edge;
step S2: selecting a reliability threshold value, and filtering out edges with reliability lower than the threshold value;
and step S3: calculate the relevancy score between every two IDs:
wherein i and j represent two different IDs, P k (i, j) represents the confidence of each edge between i and j, K represents the number of edges between i and j, and Score (i, j) represents the relevance Score between i and j;
and step S4: sorting the candidate associated IDs of each ID according to the magnitude of the association degree score;
step S5: selecting a candidate association ID corresponding to the maximum association degree score as a call-through ID of the ID:
AssocID(i)＝argmax j Score(i，j)，
wherein i and j represent two different IDs respectively, and j is a candidate associated ID corresponding to the maximum association degree score, namely j is the call-through ID of i.
2. The big-data-based ID punch-through method according to claim 1, wherein the association relationship comprises device information and spatio-temporal information.
3. The big-data-based ID punch-through method as claimed in claim 2, wherein the device information includes hardware information including brand, model, screen size and sensor model, and software information including browser ua and operating system version, and the spatiotemporal information includes time, geographical location information and ip address.
4. An ID punch-through device based on big data is characterized by comprising:
the credibility calculation unit is used for acquiring behavior data of different IDs (identities), wherein the behavior data comprises various association relation information, the same association relation between different IDs is used as one edge, the credibility of each edge between every two IDs is respectively calculated, and the credibility is in inverse proportion to the product of the number of different types of IDs associated with the edge;
the filtering unit is used for selecting a reliability threshold value and filtering edges with reliability lower than the threshold value;
a relevancy calculating unit for calculating relevancy scores between each two IDs:
wherein i and j represent two different IDs, P k (i, j) represents the confidence of each edge between i and j, K represents the number of edges between i and j, and Score (i, j) represents the relevance Score between i and j;
the sorting unit is used for sorting the candidate associated IDs of each ID according to the degree of the association score;
and the association unit is used for selecting the candidate association ID corresponding to the maximum association degree score as the getting-through ID of the ID:
AssocID(i)＝argmax j Score(i，j)，
wherein i and j represent two different IDs respectively, and j is a candidate associated ID corresponding to the maximum association degree score, namely j is the call-through ID of i.
5. An electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the big data based ID punch-through method of any of claims 1-3.
6. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the big data based ID punch-through method according to any of claims 1-3.
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| CN115794836A (en) * | 2023-01-09 | 2023-03-14 | 北京数势云创科技有限公司 | ID making-up method and device based on graph network, electronic device and storage medium |
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| CN115794836A (en) * | 2023-01-09 | 2023-03-14 | 北京数势云创科技有限公司 | ID making-up method and device based on graph network, electronic device and storage medium |
| CN115794836B (en) * | 2023-01-09 | 2023-06-09 | 北京数势云创科技有限公司 | ID (identity) opening method and device based on graph network, electronic setting and storage medium |
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